# Pooling Engram Conditional Memory in Large Language Models using CXL

arXiv: https://arxiv.org/abs/2603.10087 · HTML: https://mlsanity.com/paper/2603.10087/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2026.

## Built on by (most cited first, 2 of 2)

- [Conditional Memory Enhanced Item Representation for Generative Recommendation](https://mlsanity.com/paper/2605.11447/) (2026)
- [FactorEngram: Factorized N-gram Memory with Basis-Level Gating for Language Models](https://mlsanity.com/paper/2609.35578/) (2026)

## Abstract (arXiv)

Engram conditional memory has emerged as a promising component for LLMs by decoupling static knowledge lookup from dynamic computation. Since Engram exhibits sparse access patterns and supports prefetching, its massive embedding tables are well-suited for offloading to lower-tier memory. In this paper, we propose using Compute Express Link (CXL) memory pool for Engram storage. Compared to RDMA, CXL provides fine-grained and low-latency access required by minimal and discrete retrieval patterns of Engram. We integrate the CXL-based Engram pool into SGLang, achieving near-DRAM end-to-end performance. This provides a scalable and cost-efficient storage solution for future Engram-integrated LLMs without compromising inference performance.